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<li class="navelem"><a class="el" href="dir_5956a3e80a20e8e03eb577bedb92689f.html">gpu</a></li><li class="navelem"><a class="el" href="dir_2be73404b46ec2282840cd36fdb9a907.html">impl</a></li>  </ul>
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<div class="title">IVFPQ.cu</div>  </div>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/**</span></div>
<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Copyright (c) Facebook, Inc. and its affiliates.</span></div>
<div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> * This source code is licensed under the MIT license found in the</span></div>
<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> * LICENSE file in the root directory of this source tree.</span></div>
<div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment"> */</span></div>
<div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;</div>
<div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;</div>
<div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="preprocessor">#include &quot;IVFPQ.cuh&quot;</span></div>
<div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="preprocessor">#include &quot;../GpuResources.h&quot;</span></div>
<div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="preprocessor">#include &quot;BroadcastSum.cuh&quot;</span></div>
<div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="preprocessor">#include &quot;Distance.cuh&quot;</span></div>
<div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="preprocessor">#include &quot;FlatIndex.cuh&quot;</span></div>
<div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="preprocessor">#include &quot;InvertedListAppend.cuh&quot;</span></div>
<div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="preprocessor">#include &quot;L2Norm.cuh&quot;</span></div>
<div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;<span class="preprocessor">#include &quot;PQCodeDistances.cuh&quot;</span></div>
<div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;<span class="preprocessor">#include &quot;PQScanMultiPassNoPrecomputed.cuh&quot;</span></div>
<div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;<span class="preprocessor">#include &quot;PQScanMultiPassPrecomputed.cuh&quot;</span></div>
<div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<span class="preprocessor">#include &quot;RemapIndices.h&quot;</span></div>
<div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;<span class="preprocessor">#include &quot;VectorResidual.cuh&quot;</span></div>
<div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;<span class="preprocessor">#include &quot;../utils/DeviceDefs.cuh&quot;</span></div>
<div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;<span class="preprocessor">#include &quot;../utils/DeviceUtils.h&quot;</span></div>
<div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;<span class="preprocessor">#include &quot;../utils/HostTensor.cuh&quot;</span></div>
<div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;<span class="preprocessor">#include &quot;../utils/MatrixMult.cuh&quot;</span></div>
<div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;<span class="preprocessor">#include &quot;../utils/NoTypeTensor.cuh&quot;</span></div>
<div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;<span class="preprocessor">#include &quot;../utils/Transpose.cuh&quot;</span></div>
<div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;<span class="preprocessor">#include &lt;limits&gt;</span></div>
<div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;<span class="preprocessor">#include &lt;thrust/host_vector.h&gt;</span></div>
<div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;<span class="preprocessor">#include &lt;unordered_map&gt;</span></div>
<div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;</div>
<div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;<span class="keyword">namespace </span>faiss { <span class="keyword">namespace </span>gpu {</div>
<div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;</div>
<div class="line"><a name="l00033"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1IVFPQ.html#a18cfe8bf2178468f3372727d0b0bbc33">   33</a></span>&#160;<a class="code" href="classfaiss_1_1gpu_1_1IVFPQ.html#a18cfe8bf2178468f3372727d0b0bbc33">IVFPQ::IVFPQ</a>(<a class="code" href="classfaiss_1_1gpu_1_1GpuResources.html">GpuResources</a>* resources,</div>
<div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;             <a class="code" href="classfaiss_1_1gpu_1_1FlatIndex.html">FlatIndex</a>* quantizer,</div>
<div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;             <span class="keywordtype">int</span> numSubQuantizers,</div>
<div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;             <span class="keywordtype">int</span> bitsPerSubQuantizer,</div>
<div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;             <span class="keywordtype">float</span>* pqCentroidData,</div>
<div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;             IndicesOptions indicesOptions,</div>
<div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;             <span class="keywordtype">bool</span> useFloat16LookupTables,</div>
<div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;             MemorySpace space) :</div>
<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;    <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html">IVFBase</a>(resources,</div>
<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;            quantizer,</div>
<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;            numSubQuantizers,</div>
<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;            indicesOptions,</div>
<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;            space),</div>
<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;    numSubQuantizers_(numSubQuantizers),</div>
<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;    bitsPerSubQuantizer_(bitsPerSubQuantizer),</div>
<div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;    numSubQuantizerCodes_(utils::pow2(bitsPerSubQuantizer_)),</div>
<div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;    dimPerSubQuantizer_(dim_ / numSubQuantizers),</div>
<div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;    precomputedCodes_(false),</div>
<div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;    useFloat16LookupTables_(useFloat16LookupTables) {</div>
<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;  FAISS_ASSERT(pqCentroidData);</div>
<div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;</div>
<div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;  FAISS_ASSERT(bitsPerSubQuantizer_ &lt;= 8);</div>
<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;  FAISS_ASSERT(<a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#aba3e3cfa469e5187f2d553fff10e0250">dim_</a> % numSubQuantizers_ == 0);</div>
<div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;  FAISS_ASSERT(<a class="code" href="classfaiss_1_1gpu_1_1IVFPQ.html#adb58eeacdceb0e0fde1820ca7f116e05">isSupportedPQCodeLength</a>(<a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a319568b832518392fed33ea4f8bfc613">bytesPerVector_</a>));</div>
<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;</div>
<div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;<span class="preprocessor">#ifndef FAISS_USE_FLOAT16</span></div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;<span class="preprocessor"></span>  FAISS_ASSERT(!useFloat16LookupTables_);</div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;<span class="preprocessor">#endif</span></div>
<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;<span class="preprocessor"></span></div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;  setPQCentroids_(pqCentroidData);</div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;}</div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;</div>
<div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;IVFPQ::~IVFPQ() {</div>
<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;}</div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;</div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;</div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;<span class="keywordtype">bool</span></div>
<div class="line"><a name="l00070"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1IVFPQ.html#adb58eeacdceb0e0fde1820ca7f116e05">   70</a></span>&#160;<a class="code" href="classfaiss_1_1gpu_1_1IVFPQ.html#adb58eeacdceb0e0fde1820ca7f116e05">IVFPQ::isSupportedPQCodeLength</a>(<span class="keywordtype">int</span> size) {</div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;  <span class="keywordflow">switch</span> (size) {</div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;    <span class="keywordflow">case</span> 1:</div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;    <span class="keywordflow">case</span> 2:</div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;    <span class="keywordflow">case</span> 3:</div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;    <span class="keywordflow">case</span> 4:</div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;    <span class="keywordflow">case</span> 8:</div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;    <span class="keywordflow">case</span> 12:</div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;    <span class="keywordflow">case</span> 16:</div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;    <span class="keywordflow">case</span> 20:</div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;    <span class="keywordflow">case</span> 24:</div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;    <span class="keywordflow">case</span> 28:</div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;    <span class="keywordflow">case</span> 32:</div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;    <span class="keywordflow">case</span> 40:</div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;    <span class="keywordflow">case</span> 48:</div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;    <span class="keywordflow">case</span> 56: <span class="comment">// only supported with float16</span></div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;    <span class="keywordflow">case</span> 64: <span class="comment">// only supported with float16</span></div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;    <span class="keywordflow">case</span> 96: <span class="comment">// only supported with float16</span></div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;      <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;    <span class="keywordflow">default</span>:</div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;      <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;  }</div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;}</div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;</div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;<span class="keywordtype">bool</span></div>
<div class="line"><a name="l00095"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1IVFPQ.html#a0eedf0295ad73125ee1254173a176674">   95</a></span>&#160;<a class="code" href="classfaiss_1_1gpu_1_1IVFPQ.html#a0eedf0295ad73125ee1254173a176674">IVFPQ::isSupportedNoPrecomputedSubDimSize</a>(<span class="keywordtype">int</span> dims) {</div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;  <span class="keywordflow">return</span> faiss::gpu::isSupportedNoPrecomputedSubDimSize(dims);</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;}</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;<span class="keywordtype">void</span></div>
<div class="line"><a name="l00100"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1IVFPQ.html#adcee5dbf48c3cb6b8a67f5f392e155fd">  100</a></span>&#160;<a class="code" href="classfaiss_1_1gpu_1_1IVFPQ.html#adcee5dbf48c3cb6b8a67f5f392e155fd">IVFPQ::setPrecomputedCodes</a>(<span class="keywordtype">bool</span> enable) {</div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;  <span class="keywordflow">if</span> (precomputedCodes_ != enable) {</div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;    precomputedCodes_ = enable;</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;</div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;    <span class="keywordflow">if</span> (precomputedCodes_) {</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;      precomputeCodes_();</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;    } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;      <span class="comment">// Clear out old precomputed code data</span></div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;      precomputedCode_ = std::move(<a class="code" href="classfaiss_1_1gpu_1_1DeviceTensor.html">DeviceTensor&lt;float, 3, true&gt;</a>());</div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;<span class="preprocessor">#ifdef FAISS_USE_FLOAT16</span></div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;<span class="preprocessor"></span>      precomputedCodeHalf_ = std::move(<a class="code" href="classfaiss_1_1gpu_1_1DeviceTensor.html">DeviceTensor&lt;half, 3, true&gt;</a>());</div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;<span class="preprocessor">#endif</span></div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;<span class="preprocessor"></span>    }</div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;  }</div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;}</div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;</div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;<span class="keywordtype">int</span></div>
<div class="line"><a name="l00118"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1IVFPQ.html#ab1e07b04b25569cc58c5f3f033f4dab3">  118</a></span>&#160;<a class="code" href="classfaiss_1_1gpu_1_1IVFPQ.html#ab1e07b04b25569cc58c5f3f033f4dab3">IVFPQ::classifyAndAddVectors</a>(<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor&lt;float, 2, true&gt;</a>&amp; vecs,</div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;                             <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor&lt;long, 1, true&gt;</a>&amp; indices) {</div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;  FAISS_ASSERT(vecs.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(0) == indices.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(0));</div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;  FAISS_ASSERT(vecs.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(1) == <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#aba3e3cfa469e5187f2d553fff10e0250">dim_</a>);</div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;</div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;  FAISS_ASSERT(!<a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a878114abdba07c9cf7735f9c0ed594c3">quantizer_</a>-&gt;getUseFloat16());</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;  <span class="keyword">auto</span>&amp; coarseCentroids = <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a878114abdba07c9cf7735f9c0ed594c3">quantizer_</a>-&gt;<a class="code" href="classfaiss_1_1gpu_1_1FlatIndex.html#a12058744ffb3fbcbb047872449269c06">getVectorsFloat32Ref</a>();</div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;  <span class="keyword">auto</span>&amp; mem = <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">resources_</a>-&gt;<a class="code" href="classfaiss_1_1gpu_1_1GpuResources.html#a1dda2dc3db1bd62cde6657c5cdbfb6e1">getMemoryManagerCurrentDevice</a>();</div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;  <span class="keyword">auto</span> stream = <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">resources_</a>-&gt;<a class="code" href="classfaiss_1_1gpu_1_1GpuResources.html#aa0354aa570c24e17a9f8a6a45b153ed2">getDefaultStreamCurrentDevice</a>();</div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;</div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;  <span class="comment">// Number of valid vectors that we actually add; we return this</span></div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;  <span class="keywordtype">int</span> numAdded = 0;</div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;</div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;  <span class="comment">// We don&#39;t actually need this</span></div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;  <a class="code" href="classfaiss_1_1gpu_1_1DeviceTensor.html">DeviceTensor&lt;float, 2, true&gt;</a> listDistance(mem, {vecs.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(0), 1}, stream);</div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;  <span class="comment">// We use this</span></div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;  <a class="code" href="classfaiss_1_1gpu_1_1DeviceTensor.html">DeviceTensor&lt;int, 2, true&gt;</a> listIds2d(mem, {vecs.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(0), 1}, stream);</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;  <span class="keyword">auto</span> listIds = listIds2d.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a74dbc09519c9c14479b2d18f2e5042e8">view</a>&lt;1&gt;({vecs.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(0)});</div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;</div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;  <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a878114abdba07c9cf7735f9c0ed594c3">quantizer_</a>-&gt;query(vecs, 1, listDistance, listIds2d, <span class="keyword">false</span>);</div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;</div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;  <span class="comment">// Copy the lists that we wish to append to back to the CPU</span></div>
<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;  <span class="comment">// FIXME: really this can be into pinned memory and a true async</span></div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;  <span class="comment">// copy on a different stream; we can start the copy early, but it&#39;s</span></div>
<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;  <span class="comment">// tiny</span></div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;  <a class="code" href="classfaiss_1_1gpu_1_1HostTensor.html">HostTensor&lt;int, 1, true&gt;</a> listIdsHost(listIds, stream);</div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;</div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;  <span class="comment">// Calculate the residual for each closest centroid</span></div>
<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;  <a class="code" href="classfaiss_1_1gpu_1_1DeviceTensor.html">DeviceTensor&lt;float, 2, true&gt;</a> residuals(</div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;    mem, {vecs.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(0), vecs.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(1)}, stream);</div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;</div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;  runCalcResidual(vecs, coarseCentroids, listIds, residuals, stream);</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;</div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;  <span class="comment">// Residuals are in the form</span></div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;  <span class="comment">// (vec x numSubQuantizer x dimPerSubQuantizer)</span></div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;  <span class="comment">// transpose to</span></div>
<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;  <span class="comment">// (numSubQuantizer x vec x dimPerSubQuantizer)</span></div>
<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;  <span class="keyword">auto</span> residualsView = residuals.view&lt;3&gt;(</div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;    {residuals.getSize(0), numSubQuantizers_, dimPerSubQuantizer_});</div>
<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;</div>
<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;  <a class="code" href="classfaiss_1_1gpu_1_1DeviceTensor.html">DeviceTensor&lt;float, 3, true&gt;</a> residualsTranspose(</div>
<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;    mem,</div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;    {numSubQuantizers_, residuals.getSize(0), dimPerSubQuantizer_},</div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;    stream);</div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;</div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;  runTransposeAny(residualsView, 0, 1, residualsTranspose, stream);</div>
<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;</div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;  <span class="comment">// Get the product quantizer centroids in the form</span></div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;  <span class="comment">// (numSubQuantizer x numSubQuantizerCodes x dimPerSubQuantizer)</span></div>
<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;  <span class="comment">// which is pqCentroidsMiddleCode_</span></div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;</div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;  <span class="comment">// We now have a batch operation to find the top-1 distances:</span></div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;  <span class="comment">// batch size: numSubQuantizer</span></div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;  <span class="comment">// centroids: (numSubQuantizerCodes x dimPerSubQuantizer)</span></div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;  <span class="comment">// residuals: (vec x dimPerSubQuantizer)</span></div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;  <span class="comment">// =&gt; (numSubQuantizer x vec x 1)</span></div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;</div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;  <a class="code" href="classfaiss_1_1gpu_1_1DeviceTensor.html">DeviceTensor&lt;float, 3, true&gt;</a> closestSubQDistance(</div>
<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;    mem, {numSubQuantizers_, residuals.getSize(0), 1}, stream);</div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;  <a class="code" href="classfaiss_1_1gpu_1_1DeviceTensor.html">DeviceTensor&lt;int, 3, true&gt;</a> closestSubQIndex(</div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;    mem, {numSubQuantizers_, residuals.getSize(0), 1}, stream);</div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;</div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> subQ = 0; subQ &lt; numSubQuantizers_; ++subQ) {</div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;    <span class="keyword">auto</span> closestSubQDistanceView = closestSubQDistance[subQ].<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a74dbc09519c9c14479b2d18f2e5042e8">view</a>();</div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;    <span class="keyword">auto</span> closestSubQIndexView = closestSubQIndex[subQ].view();</div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;    <span class="keyword">auto</span> pqCentroidsMiddleCodeView = pqCentroidsMiddleCode_[subQ].<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a74dbc09519c9c14479b2d18f2e5042e8">view</a>();</div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;    <span class="keyword">auto</span> residualsTransposeView = residualsTranspose[subQ].view();</div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;</div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;    runL2Distance(<a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">resources_</a>,</div>
<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;                  pqCentroidsMiddleCodeView,</div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;                  <span class="keyword">true</span>, <span class="comment">// pqCentroidsMiddleCodeView is row major</span></div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;                  <span class="keyword">nullptr</span>, <span class="comment">// no precomputed norms</span></div>
<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;                  residualsTransposeView,</div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;                  <span class="keyword">true</span>, <span class="comment">// residualsTransposeView is row major</span></div>
<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;                  1,</div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;                  closestSubQDistanceView,</div>
<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;                  closestSubQIndexView,</div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;                  <span class="comment">// We don&#39;t care about distances</span></div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;                  <span class="keyword">true</span>);</div>
<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;  }</div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;</div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;  <span class="comment">// Now, we have the nearest sub-q centroid for each slice of the</span></div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;  <span class="comment">// residual vector.</span></div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;  <span class="keyword">auto</span> closestSubQIndexView = closestSubQIndex.view&lt;2&gt;(</div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;    {numSubQuantizers_, residuals.getSize(0)});</div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;</div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;  <span class="comment">// Transpose this for easy use</span></div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;  <a class="code" href="classfaiss_1_1gpu_1_1DeviceTensor.html">DeviceTensor&lt;int, 2, true&gt;</a> encodings(</div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;    mem, {residuals.getSize(0), numSubQuantizers_}, stream);</div>
<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;</div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;  runTransposeAny(closestSubQIndexView, 0, 1, encodings, stream);</div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;</div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;  <span class="comment">// Now we add the encoded vectors to the individual lists</span></div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;  <span class="comment">// First, make sure that there is space available for adding the new</span></div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;  <span class="comment">// encoded vectors and indices</span></div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;</div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;  <span class="comment">// list id -&gt; # being added</span></div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;  std::unordered_map&lt;int, int&gt; assignCounts;</div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;</div>
<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;  <span class="comment">// vector id -&gt; offset in list</span></div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;  <span class="comment">// (we already have vector id -&gt; list id in listIds)</span></div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;  <a class="code" href="classfaiss_1_1gpu_1_1HostTensor.html">HostTensor&lt;int, 1, true&gt;</a> listOffsetHost({listIdsHost.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(0)});</div>
<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;</div>
<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; listIdsHost.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(0); ++i) {</div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;    <span class="keywordtype">int</span> listId = listIdsHost[i];</div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;</div>
<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;    <span class="comment">// Add vector could be invalid (contains NaNs etc)</span></div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;    <span class="keywordflow">if</span> (listId &lt; 0) {</div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;      listOffsetHost[i] = -1;</div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;      <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;    }</div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;</div>
<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;    FAISS_ASSERT(listId &lt; <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#accc4d96c14643e5f471220cb1e92ac70">numLists_</a>);</div>
<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;    ++numAdded;</div>
<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;</div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;    <span class="keywordtype">int</span> offset = <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a2facc7285107de1f24d3471cbcf15f26">deviceListData_</a>[listId]-&gt;size() / <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a319568b832518392fed33ea4f8bfc613">bytesPerVector_</a>;</div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;</div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;    <span class="keyword">auto</span> it = assignCounts.find(listId);</div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;    <span class="keywordflow">if</span> (it != assignCounts.end()) {</div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;      offset += it-&gt;second;</div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;      it-&gt;second++;</div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;    } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;      assignCounts[listId] = 1;</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;    }</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;</div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;    listOffsetHost[i] = offset;</div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;  }</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;  <span class="comment">// If we didn&#39;t add anything (all invalid vectors), no need to</span></div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;  <span class="comment">// continue</span></div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;  <span class="keywordflow">if</span> (numAdded == 0) {</div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;    <span class="keywordflow">return</span> 0;</div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;  }</div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;</div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;  <span class="comment">// We need to resize the data structures for the inverted lists on</span></div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;  <span class="comment">// the GPUs, which means that they might need reallocation, which</span></div>
<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;  <span class="comment">// means that their base address may change. Figure out the new base</span></div>
<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;  <span class="comment">// addresses, and update those in a batch on the device</span></div>
<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;  {</div>
<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;    <span class="comment">// Resize all of the lists that we are appending to</span></div>
<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;    <span class="keywordflow">for</span> (<span class="keyword">auto</span>&amp; counts : assignCounts) {</div>
<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;      <span class="keyword">auto</span>&amp; codes = <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a2facc7285107de1f24d3471cbcf15f26">deviceListData_</a>[counts.first];</div>
<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;      codes-&gt;resize(codes-&gt;size() + counts.second * <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a319568b832518392fed33ea4f8bfc613">bytesPerVector_</a>,</div>
<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;                    stream);</div>
<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;      <span class="keywordtype">int</span> newNumVecs = (int) (codes-&gt;size() / <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a319568b832518392fed33ea4f8bfc613">bytesPerVector_</a>);</div>
<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;</div>
<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;      <span class="keyword">auto</span>&amp; indices = deviceListIndices_[counts.first];</div>
<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;      <span class="keywordflow">if</span> ((<a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#afb6d10e23d6448c10f472b9234e0bcab">indicesOptions_</a> == INDICES_32_BIT) ||</div>
<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;          (<a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#afb6d10e23d6448c10f472b9234e0bcab">indicesOptions_</a> == INDICES_64_BIT)) {</div>
<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;        <span class="keywordtype">size_t</span> indexSize =</div>
<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;          (<a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#afb6d10e23d6448c10f472b9234e0bcab">indicesOptions_</a> == INDICES_32_BIT) ? <span class="keyword">sizeof</span>(<span class="keywordtype">int</span>) : <span class="keyword">sizeof</span>(long);</div>
<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;</div>
<div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;        indices-&gt;resize(indices-&gt;size() + counts.second * indexSize, stream);</div>
<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;      } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (<a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#afb6d10e23d6448c10f472b9234e0bcab">indicesOptions_</a> == INDICES_CPU) {</div>
<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;        <span class="comment">// indices are stored on the CPU side</span></div>
<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;        FAISS_ASSERT(counts.first &lt; <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a53f3c382a79b7f89630a85dfbc3a1fed">listOffsetToUserIndex_</a>.size());</div>
<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;</div>
<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;        <span class="keyword">auto</span>&amp; userIndices = <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a53f3c382a79b7f89630a85dfbc3a1fed">listOffsetToUserIndex_</a>[counts.first];</div>
<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;        userIndices.resize(newNumVecs);</div>
<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;      } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;        <span class="comment">// indices are not stored on the GPU or CPU side</span></div>
<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;        FAISS_ASSERT(<a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#afb6d10e23d6448c10f472b9234e0bcab">indicesOptions_</a> == INDICES_IVF);</div>
<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;      }</div>
<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;</div>
<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;      <span class="comment">// This is used by the multi-pass query to decide how much scratch</span></div>
<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;      <span class="comment">// space to allocate for intermediate results</span></div>
<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;      <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#ae25ea0901fb628844868413f51c85bda">maxListLength_</a> = std::max(<a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#ae25ea0901fb628844868413f51c85bda">maxListLength_</a>, newNumVecs);</div>
<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;    }</div>
<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;</div>
<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;    <span class="comment">// Update all pointers and sizes on the device for lists that we</span></div>
<div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;    <span class="comment">// appended to</span></div>
<div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;    {</div>
<div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;      std::vector&lt;int&gt; listIds(assignCounts.size());</div>
<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;      <span class="keywordtype">int</span> i = 0;</div>
<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;      <span class="keywordflow">for</span> (<span class="keyword">auto</span>&amp; counts : assignCounts) {</div>
<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;        listIds[i++] = counts.first;</div>
<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;      }</div>
<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;</div>
<div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;      <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#acc695610c9513952b8d234dc0db78e5c">updateDeviceListInfo_</a>(listIds, stream);</div>
<div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;    }</div>
<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;  }</div>
<div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;</div>
<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;  <span class="comment">// If we&#39;re maintaining the indices on the CPU side, update our</span></div>
<div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;  <span class="comment">// map. We already resized our map above.</span></div>
<div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;  <span class="keywordflow">if</span> (<a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#afb6d10e23d6448c10f472b9234e0bcab">indicesOptions_</a> == INDICES_CPU) {</div>
<div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;    <span class="comment">// We need to maintain the indices on the CPU side</span></div>
<div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;    <a class="code" href="classfaiss_1_1gpu_1_1HostTensor.html">HostTensor&lt;long, 1, true&gt;</a> hostIndices(indices, stream);</div>
<div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;</div>
<div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; hostIndices.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(0); ++i) {</div>
<div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;      <span class="keywordtype">int</span> listId = listIdsHost[i];</div>
<div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;</div>
<div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;      <span class="comment">// Add vector could be invalid (contains NaNs etc)</span></div>
<div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;      <span class="keywordflow">if</span> (listId &lt; 0) {</div>
<div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;        <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;      }</div>
<div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;</div>
<div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;      <span class="keywordtype">int</span> offset = listOffsetHost[i];</div>
<div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;</div>
<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;      FAISS_ASSERT(listId &lt; <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a53f3c382a79b7f89630a85dfbc3a1fed">listOffsetToUserIndex_</a>.size());</div>
<div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;      <span class="keyword">auto</span>&amp; userIndices = <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a53f3c382a79b7f89630a85dfbc3a1fed">listOffsetToUserIndex_</a>[listId];</div>
<div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;</div>
<div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;      FAISS_ASSERT(offset &lt; userIndices.size());</div>
<div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;      userIndices[offset] = hostIndices[i];</div>
<div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;    }</div>
<div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;  }</div>
<div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;</div>
<div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;  <span class="comment">// We similarly need to actually append the new encoded vectors</span></div>
<div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;  {</div>
<div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;    <a class="code" href="classfaiss_1_1gpu_1_1DeviceTensor.html">DeviceTensor&lt;int, 1, true&gt;</a> listOffset(mem, listOffsetHost, stream);</div>
<div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;</div>
<div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;    <span class="comment">// This kernel will handle appending each encoded vector + index to</span></div>
<div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;    <span class="comment">// the appropriate list</span></div>
<div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;    runIVFPQInvertedListAppend(listIds,</div>
<div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;                               listOffset,</div>
<div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;                               encodings,</div>
<div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;                               indices,</div>
<div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;                               <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a96240a08b42bd1913e2286d7d514fc56">deviceListDataPointers_</a>,</div>
<div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;                               <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a9aedcf0e6a20b908980ae96d73461f4c">deviceListIndexPointers_</a>,</div>
<div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;                               <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#afb6d10e23d6448c10f472b9234e0bcab">indicesOptions_</a>,</div>
<div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;                               stream);</div>
<div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;  }</div>
<div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;</div>
<div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;  <span class="keywordflow">return</span> numAdded;</div>
<div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;}</div>
<div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;</div>
<div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;<span class="keywordtype">void</span></div>
<div class="line"><a name="l00345"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1IVFPQ.html#a9992b38226dc8f92ca2691582fabb675">  345</a></span>&#160;<a class="code" href="classfaiss_1_1gpu_1_1IVFPQ.html#a9992b38226dc8f92ca2691582fabb675">IVFPQ::addCodeVectorsFromCpu</a>(<span class="keywordtype">int</span> listId,</div>
<div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;                             <span class="keyword">const</span> <span class="keywordtype">void</span>* codes,</div>
<div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;                             <span class="keyword">const</span> <span class="keywordtype">long</span>* indices,</div>
<div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;                             <span class="keywordtype">size_t</span> numVecs) {</div>
<div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;  <span class="comment">// This list must already exist</span></div>
<div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;  FAISS_ASSERT(listId &lt; <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a2facc7285107de1f24d3471cbcf15f26">deviceListData_</a>.size());</div>
<div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;  <span class="keyword">auto</span> stream = <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">resources_</a>-&gt;<a class="code" href="classfaiss_1_1gpu_1_1GpuResources.html#aa0354aa570c24e17a9f8a6a45b153ed2">getDefaultStreamCurrentDevice</a>();</div>
<div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;</div>
<div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;  <span class="comment">// If there&#39;s nothing to add, then there&#39;s nothing we have to do</span></div>
<div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;  <span class="keywordflow">if</span> (numVecs == 0) {</div>
<div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;  }</div>
<div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;</div>
<div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;  <span class="keywordtype">size_t</span> lengthInBytes = numVecs * <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a319568b832518392fed33ea4f8bfc613">bytesPerVector_</a>;</div>
<div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;</div>
<div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;  <span class="keyword">auto</span>&amp; listCodes = <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a2facc7285107de1f24d3471cbcf15f26">deviceListData_</a>[listId];</div>
<div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;  <span class="keyword">auto</span> prevCodeData = listCodes-&gt;data();</div>
<div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;</div>
<div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;  <span class="comment">// We only have int32 length representations on the GPU per each</span></div>
<div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;  <span class="comment">// list; the length is in sizeof(char)</span></div>
<div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;  FAISS_ASSERT(listCodes-&gt;size() % bytesPerVector_ == 0);</div>
<div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;  FAISS_ASSERT(listCodes-&gt;size() + lengthInBytes &lt;=</div>
<div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;               (size_t) std::numeric_limits&lt;int&gt;::max());</div>
<div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;</div>
<div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;  listCodes-&gt;append((<span class="keywordtype">unsigned</span> <span class="keywordtype">char</span>*) codes,</div>
<div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;                    lengthInBytes,</div>
<div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;                    stream,</div>
<div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;                    <span class="keyword">true</span> <span class="comment">/* exact reserved size */</span>);</div>
<div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;</div>
<div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;  <span class="comment">// Handle the indices as well</span></div>
<div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;  <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a5027720549de98f4e609d6339099df35">addIndicesFromCpu_</a>(listId, indices, numVecs);</div>
<div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;</div>
<div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;  <span class="comment">// This list address may have changed due to vector resizing, but</span></div>
<div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;  <span class="comment">// only bother updating it on the device if it has changed</span></div>
<div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;  <span class="keywordflow">if</span> (prevCodeData != listCodes-&gt;data()) {</div>
<div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;    <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a96240a08b42bd1913e2286d7d514fc56">deviceListDataPointers_</a>[listId] = listCodes-&gt;data();</div>
<div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;  }</div>
<div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;</div>
<div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;  <span class="comment">// And our size has changed too</span></div>
<div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;  <span class="keywordtype">int</span> listLength = listCodes-&gt;size() / <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a319568b832518392fed33ea4f8bfc613">bytesPerVector_</a>;</div>
<div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;  <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a3a1c2031a4763f7d55bc8a400c63af66">deviceListLengths_</a>[listId] = listLength;</div>
<div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;</div>
<div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;  <span class="comment">// We update this as well, since the multi-pass algorithm uses it</span></div>
<div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;  <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#ae25ea0901fb628844868413f51c85bda">maxListLength_</a> = std::max(<a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#ae25ea0901fb628844868413f51c85bda">maxListLength_</a>, listLength);</div>
<div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;</div>
<div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;  <span class="comment">// device_vector add is potentially happening on a different stream</span></div>
<div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;  <span class="comment">// than our default stream</span></div>
<div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;  <span class="keywordflow">if</span> (<a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">resources_</a>-&gt;<a class="code" href="classfaiss_1_1gpu_1_1GpuResources.html#aa0354aa570c24e17a9f8a6a45b153ed2">getDefaultStreamCurrentDevice</a>() != 0) {</div>
<div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;    streamWait({stream}, {0});</div>
<div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;  }</div>
<div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;}</div>
<div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;</div>
<div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;<span class="keywordtype">void</span></div>
<div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;IVFPQ::setPQCentroids_(<span class="keywordtype">float</span>* data) {</div>
<div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;  <span class="keywordtype">size_t</span> pqSize =</div>
<div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;    numSubQuantizers_ * numSubQuantizerCodes_ * dimPerSubQuantizer_;</div>
<div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;</div>
<div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;  <span class="comment">// Make sure the data is on the host</span></div>
<div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;  <span class="comment">// FIXME: why are we doing this?</span></div>
<div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160;  thrust::host_vector&lt;float&gt; hostMemory;</div>
<div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;  hostMemory.insert(hostMemory.end(), data, data + pqSize);</div>
<div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;</div>
<div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;  <a class="code" href="classfaiss_1_1gpu_1_1HostTensor.html">HostTensor&lt;float, 3, true&gt;</a> pqHost(</div>
<div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;    hostMemory.data(),</div>
<div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;    {numSubQuantizers_, numSubQuantizerCodes_, dimPerSubQuantizer_});</div>
<div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;  DeviceTensor&lt;float, 3, true&gt; pqDevice(</div>
<div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;    pqHost,</div>
<div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;    <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">resources_</a>-&gt;<a class="code" href="classfaiss_1_1gpu_1_1GpuResources.html#aa0354aa570c24e17a9f8a6a45b153ed2">getDefaultStreamCurrentDevice</a>());</div>
<div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;</div>
<div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;  DeviceTensor&lt;float, 3, true&gt; pqDeviceTranspose(</div>
<div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;    {numSubQuantizers_, dimPerSubQuantizer_, numSubQuantizerCodes_});</div>
<div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;  runTransposeAny(pqDevice, 1, 2, pqDeviceTranspose,</div>
<div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;                  <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">resources_</a>-&gt;<a class="code" href="classfaiss_1_1gpu_1_1GpuResources.html#aa0354aa570c24e17a9f8a6a45b153ed2">getDefaultStreamCurrentDevice</a>());</div>
<div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;</div>
<div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;  pqCentroidsInnermostCode_ = std::move(pqDeviceTranspose);</div>
<div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;</div>
<div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;  <span class="comment">// Also maintain the PQ centroids in the form</span></div>
<div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;  <span class="comment">// (sub q)(code id)(sub dim)</span></div>
<div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;  DeviceTensor&lt;float, 3, true&gt; pqCentroidsMiddleCode(</div>
<div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;    {numSubQuantizers_, numSubQuantizerCodes_, dimPerSubQuantizer_});</div>
<div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;  runTransposeAny(pqCentroidsInnermostCode_, 1, 2, pqCentroidsMiddleCode,</div>
<div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;                  <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">resources_</a>-&gt;<a class="code" href="classfaiss_1_1gpu_1_1GpuResources.html#aa0354aa570c24e17a9f8a6a45b153ed2">getDefaultStreamCurrentDevice</a>());</div>
<div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;</div>
<div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;  pqCentroidsMiddleCode_ = std::move(pqCentroidsMiddleCode);</div>
<div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;}</div>
<div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;</div>
<div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;<span class="keywordtype">void</span></div>
<div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;IVFPQ::precomputeCodes_() {</div>
<div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;  <span class="comment">//</span></div>
<div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;  <span class="comment">//    d = || x - y_C ||^2 + || y_R ||^2 + 2 * (y_C|y_R) - 2 * (x|y_R)</span></div>
<div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;  <span class="comment">//        ---------------   ---------------------------       -------</span></div>
<div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;  <span class="comment">//            term 1                 term 2                   term 3</span></div>
<div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;  <span class="comment">//</span></div>
<div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;</div>
<div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;  <span class="comment">// Terms 1 and 3 are available only at query time. We compute term 2</span></div>
<div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;  <span class="comment">// here.</span></div>
<div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;  FAISS_ASSERT(!<a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a878114abdba07c9cf7735f9c0ed594c3">quantizer_</a>-&gt;getUseFloat16());</div>
<div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;  <span class="keyword">auto</span>&amp; coarseCentroids = <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a878114abdba07c9cf7735f9c0ed594c3">quantizer_</a>-&gt;<a class="code" href="classfaiss_1_1gpu_1_1FlatIndex.html#a12058744ffb3fbcbb047872449269c06">getVectorsFloat32Ref</a>();</div>
<div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;</div>
<div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;  <span class="comment">// Compute ||y_R||^2 by treating</span></div>
<div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;  <span class="comment">// (sub q)(code id)(sub dim) as (sub q * code id)(sub dim)</span></div>
<div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;  <span class="keyword">auto</span> pqCentroidsMiddleCodeView =</div>
<div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160;    pqCentroidsMiddleCode_.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a74dbc09519c9c14479b2d18f2e5042e8">view</a>&lt;2&gt;(</div>
<div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;      {numSubQuantizers_ * numSubQuantizerCodes_, dimPerSubQuantizer_});</div>
<div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160;  DeviceTensor&lt;float, 1, true&gt; subQuantizerNorms(</div>
<div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;    {numSubQuantizers_ * numSubQuantizerCodes_});</div>
<div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;</div>
<div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;  runL2Norm(pqCentroidsMiddleCodeView, <span class="keyword">true</span>,</div>
<div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;            subQuantizerNorms, <span class="keyword">true</span>,</div>
<div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;            <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">resources_</a>-&gt;<a class="code" href="classfaiss_1_1gpu_1_1GpuResources.html#aa0354aa570c24e17a9f8a6a45b153ed2">getDefaultStreamCurrentDevice</a>());</div>
<div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;</div>
<div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;  <span class="comment">// Compute 2 * (y_C|y_R) via batch matrix multiplication</span></div>
<div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;  <span class="comment">// batch size (sub q) x {(centroid id)(sub dim) x (code id)(sub dim)&#39;}</span></div>
<div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;  <span class="comment">//         =&gt; (sub q) x {(centroid id)(code id)}</span></div>
<div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;  <span class="comment">//         =&gt; (sub q)(centroid id)(code id)</span></div>
<div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160;</div>
<div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160;  <span class="comment">// View (centroid id)(dim) as</span></div>
<div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160;  <span class="comment">//      (centroid id)(sub q)(dim)</span></div>
<div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160;  <span class="comment">// Transpose (centroid id)(sub q)(sub dim) to</span></div>
<div class="line"><a name="l00464"></a><span class="lineno">  464</span>&#160;  <span class="comment">//           (sub q)(centroid id)(sub dim)</span></div>
<div class="line"><a name="l00465"></a><span class="lineno">  465</span>&#160;  <span class="keyword">auto</span> centroidView = coarseCentroids.view&lt;3&gt;(</div>
<div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160;    {coarseCentroids.getSize(0), numSubQuantizers_, dimPerSubQuantizer_});</div>
<div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160;  DeviceTensor&lt;float, 3, true&gt; centroidsTransposed(</div>
<div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;    {numSubQuantizers_, coarseCentroids.getSize(0), dimPerSubQuantizer_});</div>
<div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160;</div>
<div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;  runTransposeAny(centroidView, 0, 1, centroidsTransposed,</div>
<div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160;                  <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">resources_</a>-&gt;<a class="code" href="classfaiss_1_1gpu_1_1GpuResources.html#aa0354aa570c24e17a9f8a6a45b153ed2">getDefaultStreamCurrentDevice</a>());</div>
<div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;</div>
<div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;  DeviceTensor&lt;float, 3, true&gt; coarsePQProduct(</div>
<div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;    {numSubQuantizers_, coarseCentroids.getSize(0), numSubQuantizerCodes_});</div>
<div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;</div>
<div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;  runIteratedMatrixMult(coarsePQProduct, <span class="keyword">false</span>,</div>
<div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;                        centroidsTransposed, <span class="keyword">false</span>,</div>
<div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;                        pqCentroidsMiddleCode_, <span class="keyword">true</span>,</div>
<div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;                        2.0f, 0.0f,</div>
<div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;                        <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">resources_</a>-&gt;<a class="code" href="classfaiss_1_1gpu_1_1GpuResources.html#a00cb7bcbc5f1a00da673f30749149b12">getBlasHandleCurrentDevice</a>(),</div>
<div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;                        <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">resources_</a>-&gt;<a class="code" href="classfaiss_1_1gpu_1_1GpuResources.html#aa0354aa570c24e17a9f8a6a45b153ed2">getDefaultStreamCurrentDevice</a>());</div>
<div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;</div>
<div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;  <span class="comment">// Transpose (sub q)(centroid id)(code id) to</span></div>
<div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;  <span class="comment">//           (centroid id)(sub q)(code id)</span></div>
<div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160;  DeviceTensor&lt;float, 3, true&gt; coarsePQProductTransposed(</div>
<div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160;    {coarseCentroids.getSize(0), numSubQuantizers_, numSubQuantizerCodes_});</div>
<div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;  runTransposeAny(coarsePQProduct, 0, 1, coarsePQProductTransposed,</div>
<div class="line"><a name="l00488"></a><span class="lineno">  488</span>&#160;                  <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">resources_</a>-&gt;<a class="code" href="classfaiss_1_1gpu_1_1GpuResources.html#aa0354aa570c24e17a9f8a6a45b153ed2">getDefaultStreamCurrentDevice</a>());</div>
<div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160;</div>
<div class="line"><a name="l00490"></a><span class="lineno">  490</span>&#160;  <span class="comment">// View (centroid id)(sub q)(code id) as</span></div>
<div class="line"><a name="l00491"></a><span class="lineno">  491</span>&#160;  <span class="comment">//      (centroid id)(sub q * code id)</span></div>
<div class="line"><a name="l00492"></a><span class="lineno">  492</span>&#160;  <span class="keyword">auto</span> coarsePQProductTransposedView = coarsePQProductTransposed.view&lt;2&gt;(</div>
<div class="line"><a name="l00493"></a><span class="lineno">  493</span>&#160;    {coarseCentroids.getSize(0), numSubQuantizers_ * numSubQuantizerCodes_});</div>
<div class="line"><a name="l00494"></a><span class="lineno">  494</span>&#160;</div>
<div class="line"><a name="l00495"></a><span class="lineno">  495</span>&#160;  <span class="comment">// Sum || y_R ||^2 + 2 * (y_C|y_R)</span></div>
<div class="line"><a name="l00496"></a><span class="lineno">  496</span>&#160;  <span class="comment">// i.e., add norms                              (sub q * code id)</span></div>
<div class="line"><a name="l00497"></a><span class="lineno">  497</span>&#160;  <span class="comment">// along columns of inner product  (centroid id)(sub q * code id)</span></div>
<div class="line"><a name="l00498"></a><span class="lineno">  498</span>&#160;  runSumAlongColumns(subQuantizerNorms, coarsePQProductTransposedView,</div>
<div class="line"><a name="l00499"></a><span class="lineno">  499</span>&#160;                     <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">resources_</a>-&gt;<a class="code" href="classfaiss_1_1gpu_1_1GpuResources.html#aa0354aa570c24e17a9f8a6a45b153ed2">getDefaultStreamCurrentDevice</a>());</div>
<div class="line"><a name="l00500"></a><span class="lineno">  500</span>&#160;</div>
<div class="line"><a name="l00501"></a><span class="lineno">  501</span>&#160;<span class="preprocessor">#ifdef FAISS_USE_FLOAT16</span></div>
<div class="line"><a name="l00502"></a><span class="lineno">  502</span>&#160;<span class="preprocessor"></span>  <span class="keywordflow">if</span> (useFloat16LookupTables_) {</div>
<div class="line"><a name="l00503"></a><span class="lineno">  503</span>&#160;    precomputedCodeHalf_ = toHalf(<a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">resources_</a>,</div>
<div class="line"><a name="l00504"></a><span class="lineno">  504</span>&#160;                                  <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">resources_</a>-&gt;<a class="code" href="classfaiss_1_1gpu_1_1GpuResources.html#aa0354aa570c24e17a9f8a6a45b153ed2">getDefaultStreamCurrentDevice</a>(),</div>
<div class="line"><a name="l00505"></a><span class="lineno">  505</span>&#160;                                  coarsePQProductTransposed);</div>
<div class="line"><a name="l00506"></a><span class="lineno">  506</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00507"></a><span class="lineno">  507</span>&#160;  }</div>
<div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160;<span class="preprocessor">#endif</span></div>
<div class="line"><a name="l00509"></a><span class="lineno">  509</span>&#160;<span class="preprocessor"></span></div>
<div class="line"><a name="l00510"></a><span class="lineno">  510</span>&#160;  <span class="comment">// We added into the view, so `coarsePQProductTransposed` is now our</span></div>
<div class="line"><a name="l00511"></a><span class="lineno">  511</span>&#160;  <span class="comment">// precomputed term 2.</span></div>
<div class="line"><a name="l00512"></a><span class="lineno">  512</span>&#160;  precomputedCode_ = std::move(coarsePQProductTransposed);</div>
<div class="line"><a name="l00513"></a><span class="lineno">  513</span>&#160;}</div>
<div class="line"><a name="l00514"></a><span class="lineno">  514</span>&#160;</div>
<div class="line"><a name="l00515"></a><span class="lineno">  515</span>&#160;<span class="keywordtype">void</span></div>
<div class="line"><a name="l00516"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1IVFPQ.html#ab0c458aab9a3d903f31b0e63ce16e623">  516</a></span>&#160;<a class="code" href="classfaiss_1_1gpu_1_1IVFPQ.html#ab0c458aab9a3d903f31b0e63ce16e623">IVFPQ::query</a>(<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor&lt;float, 2, true&gt;</a>&amp; queries,</div>
<div class="line"><a name="l00517"></a><span class="lineno">  517</span>&#160;             <span class="keywordtype">int</span> nprobe,</div>
<div class="line"><a name="l00518"></a><span class="lineno">  518</span>&#160;             <span class="keywordtype">int</span> k,</div>
<div class="line"><a name="l00519"></a><span class="lineno">  519</span>&#160;             <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor&lt;float, 2, true&gt;</a>&amp; outDistances,</div>
<div class="line"><a name="l00520"></a><span class="lineno">  520</span>&#160;             <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor&lt;long, 2, true&gt;</a>&amp; outIndices) {</div>
<div class="line"><a name="l00521"></a><span class="lineno">  521</span>&#160;  <span class="comment">// These are caught at a higher level</span></div>
<div class="line"><a name="l00522"></a><span class="lineno">  522</span>&#160;  FAISS_ASSERT(nprobe &lt;= GPU_MAX_SELECTION_K);</div>
<div class="line"><a name="l00523"></a><span class="lineno">  523</span>&#160;  FAISS_ASSERT(k &lt;= GPU_MAX_SELECTION_K);</div>
<div class="line"><a name="l00524"></a><span class="lineno">  524</span>&#160;</div>
<div class="line"><a name="l00525"></a><span class="lineno">  525</span>&#160;  <span class="keyword">auto</span>&amp; mem = <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">resources_</a>-&gt;<a class="code" href="classfaiss_1_1gpu_1_1GpuResources.html#a1dda2dc3db1bd62cde6657c5cdbfb6e1">getMemoryManagerCurrentDevice</a>();</div>
<div class="line"><a name="l00526"></a><span class="lineno">  526</span>&#160;  <span class="keyword">auto</span> stream = <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">resources_</a>-&gt;<a class="code" href="classfaiss_1_1gpu_1_1GpuResources.html#aa0354aa570c24e17a9f8a6a45b153ed2">getDefaultStreamCurrentDevice</a>();</div>
<div class="line"><a name="l00527"></a><span class="lineno">  527</span>&#160;  nprobe = std::min(nprobe, <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a878114abdba07c9cf7735f9c0ed594c3">quantizer_</a>-&gt;<a class="code" href="classfaiss_1_1gpu_1_1FlatIndex.html#a6988df17792dae30f24cc859728777e6">getSize</a>());</div>
<div class="line"><a name="l00528"></a><span class="lineno">  528</span>&#160;</div>
<div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160;  FAISS_ASSERT(queries.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(1) == <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#aba3e3cfa469e5187f2d553fff10e0250">dim_</a>);</div>
<div class="line"><a name="l00530"></a><span class="lineno">  530</span>&#160;  FAISS_ASSERT(outDistances.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(0) == queries.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(0));</div>
<div class="line"><a name="l00531"></a><span class="lineno">  531</span>&#160;  FAISS_ASSERT(outIndices.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(0) == queries.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(0));</div>
<div class="line"><a name="l00532"></a><span class="lineno">  532</span>&#160;</div>
<div class="line"><a name="l00533"></a><span class="lineno">  533</span>&#160;  <span class="comment">// Reserve space for the closest coarse centroids</span></div>
<div class="line"><a name="l00534"></a><span class="lineno">  534</span>&#160;  <a class="code" href="classfaiss_1_1gpu_1_1DeviceTensor.html">DeviceTensor&lt;float, 2, true&gt;</a></div>
<div class="line"><a name="l00535"></a><span class="lineno">  535</span>&#160;    coarseDistances(mem, {queries.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(0), nprobe}, stream);</div>
<div class="line"><a name="l00536"></a><span class="lineno">  536</span>&#160;  <a class="code" href="classfaiss_1_1gpu_1_1DeviceTensor.html">DeviceTensor&lt;int, 2, true&gt;</a></div>
<div class="line"><a name="l00537"></a><span class="lineno">  537</span>&#160;    coarseIndices(mem, {queries.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(0), nprobe}, stream);</div>
<div class="line"><a name="l00538"></a><span class="lineno">  538</span>&#160;</div>
<div class="line"><a name="l00539"></a><span class="lineno">  539</span>&#160;  <span class="comment">// Find the `nprobe` closest coarse centroids; we can use int</span></div>
<div class="line"><a name="l00540"></a><span class="lineno">  540</span>&#160;  <span class="comment">// indices both internally and externally</span></div>
<div class="line"><a name="l00541"></a><span class="lineno">  541</span>&#160;  <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a878114abdba07c9cf7735f9c0ed594c3">quantizer_</a>-&gt;query(queries,</div>
<div class="line"><a name="l00542"></a><span class="lineno">  542</span>&#160;                    nprobe,</div>
<div class="line"><a name="l00543"></a><span class="lineno">  543</span>&#160;                    coarseDistances,</div>
<div class="line"><a name="l00544"></a><span class="lineno">  544</span>&#160;                    coarseIndices,</div>
<div class="line"><a name="l00545"></a><span class="lineno">  545</span>&#160;                    <span class="keyword">true</span>);</div>
<div class="line"><a name="l00546"></a><span class="lineno">  546</span>&#160;</div>
<div class="line"><a name="l00547"></a><span class="lineno">  547</span>&#160;  <span class="keywordflow">if</span> (precomputedCodes_) {</div>
<div class="line"><a name="l00548"></a><span class="lineno">  548</span>&#160;    runPQPrecomputedCodes_(queries,</div>
<div class="line"><a name="l00549"></a><span class="lineno">  549</span>&#160;                           coarseDistances,</div>
<div class="line"><a name="l00550"></a><span class="lineno">  550</span>&#160;                           coarseIndices,</div>
<div class="line"><a name="l00551"></a><span class="lineno">  551</span>&#160;                           k,</div>
<div class="line"><a name="l00552"></a><span class="lineno">  552</span>&#160;                           outDistances,</div>
<div class="line"><a name="l00553"></a><span class="lineno">  553</span>&#160;                           outIndices);</div>
<div class="line"><a name="l00554"></a><span class="lineno">  554</span>&#160;  } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00555"></a><span class="lineno">  555</span>&#160;    runPQNoPrecomputedCodes_(queries,</div>
<div class="line"><a name="l00556"></a><span class="lineno">  556</span>&#160;                             coarseDistances,</div>
<div class="line"><a name="l00557"></a><span class="lineno">  557</span>&#160;                             coarseIndices,</div>
<div class="line"><a name="l00558"></a><span class="lineno">  558</span>&#160;                             k,</div>
<div class="line"><a name="l00559"></a><span class="lineno">  559</span>&#160;                             outDistances,</div>
<div class="line"><a name="l00560"></a><span class="lineno">  560</span>&#160;                             outIndices);</div>
<div class="line"><a name="l00561"></a><span class="lineno">  561</span>&#160;  }</div>
<div class="line"><a name="l00562"></a><span class="lineno">  562</span>&#160;</div>
<div class="line"><a name="l00563"></a><span class="lineno">  563</span>&#160;  <span class="comment">// If the GPU isn&#39;t storing indices (they are on the CPU side), we</span></div>
<div class="line"><a name="l00564"></a><span class="lineno">  564</span>&#160;  <span class="comment">// need to perform the re-mapping here</span></div>
<div class="line"><a name="l00565"></a><span class="lineno">  565</span>&#160;  <span class="comment">// FIXME: we might ultimately be calling this function with inputs</span></div>
<div class="line"><a name="l00566"></a><span class="lineno">  566</span>&#160;  <span class="comment">// from the CPU, these are unnecessary copies</span></div>
<div class="line"><a name="l00567"></a><span class="lineno">  567</span>&#160;  <span class="keywordflow">if</span> (<a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#afb6d10e23d6448c10f472b9234e0bcab">indicesOptions_</a> == INDICES_CPU) {</div>
<div class="line"><a name="l00568"></a><span class="lineno">  568</span>&#160;    <a class="code" href="classfaiss_1_1gpu_1_1HostTensor.html">HostTensor&lt;long, 2, true&gt;</a> hostOutIndices(outIndices, stream);</div>
<div class="line"><a name="l00569"></a><span class="lineno">  569</span>&#160;</div>
<div class="line"><a name="l00570"></a><span class="lineno">  570</span>&#160;    ivfOffsetToUserIndex(hostOutIndices.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a50411ce4d0fa32ef715e3321b6e33212">data</a>(),</div>
<div class="line"><a name="l00571"></a><span class="lineno">  571</span>&#160;                         <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#accc4d96c14643e5f471220cb1e92ac70">numLists_</a>,</div>
<div class="line"><a name="l00572"></a><span class="lineno">  572</span>&#160;                         hostOutIndices.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(0),</div>
<div class="line"><a name="l00573"></a><span class="lineno">  573</span>&#160;                         hostOutIndices.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(1),</div>
<div class="line"><a name="l00574"></a><span class="lineno">  574</span>&#160;                         <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a53f3c382a79b7f89630a85dfbc3a1fed">listOffsetToUserIndex_</a>);</div>
<div class="line"><a name="l00575"></a><span class="lineno">  575</span>&#160;</div>
<div class="line"><a name="l00576"></a><span class="lineno">  576</span>&#160;    <span class="comment">// Copy back to GPU, since the input to this function is on the</span></div>
<div class="line"><a name="l00577"></a><span class="lineno">  577</span>&#160;    <span class="comment">// GPU</span></div>
<div class="line"><a name="l00578"></a><span class="lineno">  578</span>&#160;    outIndices.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6dc00c182a92389b74c89ba7fcab40d3">copyFrom</a>(hostOutIndices, stream);</div>
<div class="line"><a name="l00579"></a><span class="lineno">  579</span>&#160;  }</div>
<div class="line"><a name="l00580"></a><span class="lineno">  580</span>&#160;}</div>
<div class="line"><a name="l00581"></a><span class="lineno">  581</span>&#160;</div>
<div class="line"><a name="l00582"></a><span class="lineno">  582</span>&#160;std::vector&lt;unsigned char&gt;</div>
<div class="line"><a name="l00583"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1IVFPQ.html#a5b349dd021b11b5f48531825359b0657">  583</a></span>&#160;<a class="code" href="classfaiss_1_1gpu_1_1IVFPQ.html#a5b349dd021b11b5f48531825359b0657">IVFPQ::getListCodes</a>(<span class="keywordtype">int</span> listId)<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00584"></a><span class="lineno">  584</span>&#160;  FAISS_ASSERT(listId &lt; <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a2facc7285107de1f24d3471cbcf15f26">deviceListData_</a>.size());</div>
<div class="line"><a name="l00585"></a><span class="lineno">  585</span>&#160;</div>
<div class="line"><a name="l00586"></a><span class="lineno">  586</span>&#160;  <span class="keywordflow">return</span> <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a2facc7285107de1f24d3471cbcf15f26">deviceListData_</a>[listId]-&gt;copyToHost&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">char</span>&gt;(</div>
<div class="line"><a name="l00587"></a><span class="lineno">  587</span>&#160;    <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">resources_</a>-&gt;<a class="code" href="classfaiss_1_1gpu_1_1GpuResources.html#aa0354aa570c24e17a9f8a6a45b153ed2">getDefaultStreamCurrentDevice</a>());</div>
<div class="line"><a name="l00588"></a><span class="lineno">  588</span>&#160;}</div>
<div class="line"><a name="l00589"></a><span class="lineno">  589</span>&#160;</div>
<div class="line"><a name="l00590"></a><span class="lineno">  590</span>&#160;<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor&lt;float, 3, true&gt;</a></div>
<div class="line"><a name="l00591"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1IVFPQ.html#a3e8bff50f894c243c62e832f923e88e7">  591</a></span>&#160;<a class="code" href="classfaiss_1_1gpu_1_1IVFPQ.html#a3e8bff50f894c243c62e832f923e88e7">IVFPQ::getPQCentroids</a>() {</div>
<div class="line"><a name="l00592"></a><span class="lineno">  592</span>&#160;  <span class="keywordflow">return</span> pqCentroidsMiddleCode_;</div>
<div class="line"><a name="l00593"></a><span class="lineno">  593</span>&#160;}</div>
<div class="line"><a name="l00594"></a><span class="lineno">  594</span>&#160;</div>
<div class="line"><a name="l00595"></a><span class="lineno">  595</span>&#160;<span class="keywordtype">void</span></div>
<div class="line"><a name="l00596"></a><span class="lineno">  596</span>&#160;IVFPQ::runPQPrecomputedCodes_(</div>
<div class="line"><a name="l00597"></a><span class="lineno">  597</span>&#160;  <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor&lt;float, 2, true&gt;</a>&amp; queries,</div>
<div class="line"><a name="l00598"></a><span class="lineno">  598</span>&#160;  <a class="code" href="classfaiss_1_1gpu_1_1DeviceTensor.html">DeviceTensor&lt;float, 2, true&gt;</a>&amp; coarseDistances,</div>
<div class="line"><a name="l00599"></a><span class="lineno">  599</span>&#160;  <a class="code" href="classfaiss_1_1gpu_1_1DeviceTensor.html">DeviceTensor&lt;int, 2, true&gt;</a>&amp; coarseIndices,</div>
<div class="line"><a name="l00600"></a><span class="lineno">  600</span>&#160;  <span class="keywordtype">int</span> k,</div>
<div class="line"><a name="l00601"></a><span class="lineno">  601</span>&#160;  <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor&lt;float, 2, true&gt;</a>&amp; outDistances,</div>
<div class="line"><a name="l00602"></a><span class="lineno">  602</span>&#160;  <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor&lt;long, 2, true&gt;</a>&amp; outIndices) {</div>
<div class="line"><a name="l00603"></a><span class="lineno">  603</span>&#160;  <span class="keyword">auto</span>&amp; mem = <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">resources_</a>-&gt;<a class="code" href="classfaiss_1_1gpu_1_1GpuResources.html#a1dda2dc3db1bd62cde6657c5cdbfb6e1">getMemoryManagerCurrentDevice</a>();</div>
<div class="line"><a name="l00604"></a><span class="lineno">  604</span>&#160;  <span class="keyword">auto</span> stream = <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">resources_</a>-&gt;<a class="code" href="classfaiss_1_1gpu_1_1GpuResources.html#aa0354aa570c24e17a9f8a6a45b153ed2">getDefaultStreamCurrentDevice</a>();</div>
<div class="line"><a name="l00605"></a><span class="lineno">  605</span>&#160;</div>
<div class="line"><a name="l00606"></a><span class="lineno">  606</span>&#160;  <span class="comment">// Compute precomputed code term 3, - 2 * (x|y_R)</span></div>
<div class="line"><a name="l00607"></a><span class="lineno">  607</span>&#160;  <span class="comment">// This is done via batch MM</span></div>
<div class="line"><a name="l00608"></a><span class="lineno">  608</span>&#160;  <span class="comment">// {sub q} x {(query id)(sub dim) * (code id)(sub dim)&#39;} =&gt;</span></div>
<div class="line"><a name="l00609"></a><span class="lineno">  609</span>&#160;  <span class="comment">// {sub q} x {(query id)(code id)}</span></div>
<div class="line"><a name="l00610"></a><span class="lineno">  610</span>&#160;  <a class="code" href="classfaiss_1_1gpu_1_1DeviceTensor.html">DeviceTensor&lt;float, 3, true&gt;</a> term3Transposed(</div>
<div class="line"><a name="l00611"></a><span class="lineno">  611</span>&#160;    mem,</div>
<div class="line"><a name="l00612"></a><span class="lineno">  612</span>&#160;    {queries.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(0), numSubQuantizers_, numSubQuantizerCodes_},</div>
<div class="line"><a name="l00613"></a><span class="lineno">  613</span>&#160;    stream);</div>
<div class="line"><a name="l00614"></a><span class="lineno">  614</span>&#160;</div>
<div class="line"><a name="l00615"></a><span class="lineno">  615</span>&#160;  <span class="comment">// These allocations within are only temporary, so release them when</span></div>
<div class="line"><a name="l00616"></a><span class="lineno">  616</span>&#160;  <span class="comment">// we&#39;re done to maximize free space</span></div>
<div class="line"><a name="l00617"></a><span class="lineno">  617</span>&#160;  {</div>
<div class="line"><a name="l00618"></a><span class="lineno">  618</span>&#160;    <span class="keyword">auto</span> querySubQuantizerView = queries.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a74dbc09519c9c14479b2d18f2e5042e8">view</a>&lt;3&gt;(</div>
<div class="line"><a name="l00619"></a><span class="lineno">  619</span>&#160;      {queries.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(0), numSubQuantizers_, dimPerSubQuantizer_});</div>
<div class="line"><a name="l00620"></a><span class="lineno">  620</span>&#160;    DeviceTensor&lt;float, 3, true&gt; queriesTransposed(</div>
<div class="line"><a name="l00621"></a><span class="lineno">  621</span>&#160;      mem,</div>
<div class="line"><a name="l00622"></a><span class="lineno">  622</span>&#160;      {numSubQuantizers_, queries.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(0), dimPerSubQuantizer_},</div>
<div class="line"><a name="l00623"></a><span class="lineno">  623</span>&#160;      stream);</div>
<div class="line"><a name="l00624"></a><span class="lineno">  624</span>&#160;    runTransposeAny(querySubQuantizerView, 0, 1, queriesTransposed, stream);</div>
<div class="line"><a name="l00625"></a><span class="lineno">  625</span>&#160;</div>
<div class="line"><a name="l00626"></a><span class="lineno">  626</span>&#160;    DeviceTensor&lt;float, 3, true&gt; term3(</div>
<div class="line"><a name="l00627"></a><span class="lineno">  627</span>&#160;      mem,</div>
<div class="line"><a name="l00628"></a><span class="lineno">  628</span>&#160;      {numSubQuantizers_, queries.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(0), numSubQuantizerCodes_},</div>
<div class="line"><a name="l00629"></a><span class="lineno">  629</span>&#160;      stream);</div>
<div class="line"><a name="l00630"></a><span class="lineno">  630</span>&#160;</div>
<div class="line"><a name="l00631"></a><span class="lineno">  631</span>&#160;    runIteratedMatrixMult(term3, <span class="keyword">false</span>,</div>
<div class="line"><a name="l00632"></a><span class="lineno">  632</span>&#160;                          queriesTransposed, <span class="keyword">false</span>,</div>
<div class="line"><a name="l00633"></a><span class="lineno">  633</span>&#160;                          pqCentroidsMiddleCode_, <span class="keyword">true</span>,</div>
<div class="line"><a name="l00634"></a><span class="lineno">  634</span>&#160;                          -2.0f, 0.0f,</div>
<div class="line"><a name="l00635"></a><span class="lineno">  635</span>&#160;                          <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">resources_</a>-&gt;<a class="code" href="classfaiss_1_1gpu_1_1GpuResources.html#a00cb7bcbc5f1a00da673f30749149b12">getBlasHandleCurrentDevice</a>(),</div>
<div class="line"><a name="l00636"></a><span class="lineno">  636</span>&#160;                          stream);</div>
<div class="line"><a name="l00637"></a><span class="lineno">  637</span>&#160;</div>
<div class="line"><a name="l00638"></a><span class="lineno">  638</span>&#160;    runTransposeAny(term3, 0, 1, term3Transposed, stream);</div>
<div class="line"><a name="l00639"></a><span class="lineno">  639</span>&#160;  }</div>
<div class="line"><a name="l00640"></a><span class="lineno">  640</span>&#160;</div>
<div class="line"><a name="l00641"></a><span class="lineno">  641</span>&#160;  NoTypeTensor&lt;3, true&gt; term2;</div>
<div class="line"><a name="l00642"></a><span class="lineno">  642</span>&#160;  NoTypeTensor&lt;3, true&gt; term3;</div>
<div class="line"><a name="l00643"></a><span class="lineno">  643</span>&#160;<span class="preprocessor">#ifdef FAISS_USE_FLOAT16</span></div>
<div class="line"><a name="l00644"></a><span class="lineno">  644</span>&#160;<span class="preprocessor"></span>  DeviceTensor&lt;half, 3, true&gt; term3Half;</div>
<div class="line"><a name="l00645"></a><span class="lineno">  645</span>&#160;</div>
<div class="line"><a name="l00646"></a><span class="lineno">  646</span>&#160;  <span class="keywordflow">if</span> (useFloat16LookupTables_) {</div>
<div class="line"><a name="l00647"></a><span class="lineno">  647</span>&#160;    term3Half = toHalf(<a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">resources_</a>, stream, term3Transposed);</div>
<div class="line"><a name="l00648"></a><span class="lineno">  648</span>&#160;    term2 = NoTypeTensor&lt;3, true&gt;(precomputedCodeHalf_);</div>
<div class="line"><a name="l00649"></a><span class="lineno">  649</span>&#160;    term3 = NoTypeTensor&lt;3, true&gt;(term3Half);</div>
<div class="line"><a name="l00650"></a><span class="lineno">  650</span>&#160;  }</div>
<div class="line"><a name="l00651"></a><span class="lineno">  651</span>&#160;<span class="preprocessor">#endif</span></div>
<div class="line"><a name="l00652"></a><span class="lineno">  652</span>&#160;<span class="preprocessor"></span></div>
<div class="line"><a name="l00653"></a><span class="lineno">  653</span>&#160;  <span class="keywordflow">if</span> (!useFloat16LookupTables_) {</div>
<div class="line"><a name="l00654"></a><span class="lineno">  654</span>&#160;    term2 = NoTypeTensor&lt;3, true&gt;(precomputedCode_);</div>
<div class="line"><a name="l00655"></a><span class="lineno">  655</span>&#160;    term3 = NoTypeTensor&lt;3, true&gt;(term3Transposed);</div>
<div class="line"><a name="l00656"></a><span class="lineno">  656</span>&#160;  }</div>
<div class="line"><a name="l00657"></a><span class="lineno">  657</span>&#160;</div>
<div class="line"><a name="l00658"></a><span class="lineno">  658</span>&#160;  runPQScanMultiPassPrecomputed(queries,</div>
<div class="line"><a name="l00659"></a><span class="lineno">  659</span>&#160;                                coarseDistances, <span class="comment">// term 1</span></div>
<div class="line"><a name="l00660"></a><span class="lineno">  660</span>&#160;                                term2, <span class="comment">// term 2</span></div>
<div class="line"><a name="l00661"></a><span class="lineno">  661</span>&#160;                                term3, <span class="comment">// term 3</span></div>
<div class="line"><a name="l00662"></a><span class="lineno">  662</span>&#160;                                coarseIndices,</div>
<div class="line"><a name="l00663"></a><span class="lineno">  663</span>&#160;                                useFloat16LookupTables_,</div>
<div class="line"><a name="l00664"></a><span class="lineno">  664</span>&#160;                                <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a319568b832518392fed33ea4f8bfc613">bytesPerVector_</a>,</div>
<div class="line"><a name="l00665"></a><span class="lineno">  665</span>&#160;                                numSubQuantizers_,</div>
<div class="line"><a name="l00666"></a><span class="lineno">  666</span>&#160;                                numSubQuantizerCodes_,</div>
<div class="line"><a name="l00667"></a><span class="lineno">  667</span>&#160;                                <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a96240a08b42bd1913e2286d7d514fc56">deviceListDataPointers_</a>,</div>
<div class="line"><a name="l00668"></a><span class="lineno">  668</span>&#160;                                <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a9aedcf0e6a20b908980ae96d73461f4c">deviceListIndexPointers_</a>,</div>
<div class="line"><a name="l00669"></a><span class="lineno">  669</span>&#160;                                <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#afb6d10e23d6448c10f472b9234e0bcab">indicesOptions_</a>,</div>
<div class="line"><a name="l00670"></a><span class="lineno">  670</span>&#160;                                <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a3a1c2031a4763f7d55bc8a400c63af66">deviceListLengths_</a>,</div>
<div class="line"><a name="l00671"></a><span class="lineno">  671</span>&#160;                                <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#ae25ea0901fb628844868413f51c85bda">maxListLength_</a>,</div>
<div class="line"><a name="l00672"></a><span class="lineno">  672</span>&#160;                                k,</div>
<div class="line"><a name="l00673"></a><span class="lineno">  673</span>&#160;                                outDistances,</div>
<div class="line"><a name="l00674"></a><span class="lineno">  674</span>&#160;                                outIndices,</div>
<div class="line"><a name="l00675"></a><span class="lineno">  675</span>&#160;                                <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">resources_</a>);</div>
<div class="line"><a name="l00676"></a><span class="lineno">  676</span>&#160;}</div>
<div class="line"><a name="l00677"></a><span class="lineno">  677</span>&#160;</div>
<div class="line"><a name="l00678"></a><span class="lineno">  678</span>&#160;<span class="keywordtype">void</span></div>
<div class="line"><a name="l00679"></a><span class="lineno">  679</span>&#160;IVFPQ::runPQNoPrecomputedCodes_(</div>
<div class="line"><a name="l00680"></a><span class="lineno">  680</span>&#160;  Tensor&lt;float, 2, true&gt;&amp; queries,</div>
<div class="line"><a name="l00681"></a><span class="lineno">  681</span>&#160;  DeviceTensor&lt;float, 2, true&gt;&amp; coarseDistances,</div>
<div class="line"><a name="l00682"></a><span class="lineno">  682</span>&#160;  DeviceTensor&lt;int, 2, true&gt;&amp; coarseIndices,</div>
<div class="line"><a name="l00683"></a><span class="lineno">  683</span>&#160;  <span class="keywordtype">int</span> k,</div>
<div class="line"><a name="l00684"></a><span class="lineno">  684</span>&#160;  Tensor&lt;float, 2, true&gt;&amp; outDistances,</div>
<div class="line"><a name="l00685"></a><span class="lineno">  685</span>&#160;  Tensor&lt;long, 2, true&gt;&amp; outIndices) {</div>
<div class="line"><a name="l00686"></a><span class="lineno">  686</span>&#160;  FAISS_ASSERT(!<a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a878114abdba07c9cf7735f9c0ed594c3">quantizer_</a>-&gt;getUseFloat16());</div>
<div class="line"><a name="l00687"></a><span class="lineno">  687</span>&#160;  <span class="keyword">auto</span>&amp; coarseCentroids = <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a878114abdba07c9cf7735f9c0ed594c3">quantizer_</a>-&gt;<a class="code" href="classfaiss_1_1gpu_1_1FlatIndex.html#a12058744ffb3fbcbb047872449269c06">getVectorsFloat32Ref</a>();</div>
<div class="line"><a name="l00688"></a><span class="lineno">  688</span>&#160;</div>
<div class="line"><a name="l00689"></a><span class="lineno">  689</span>&#160;  runPQScanMultiPassNoPrecomputed(queries,</div>
<div class="line"><a name="l00690"></a><span class="lineno">  690</span>&#160;                                  coarseCentroids,</div>
<div class="line"><a name="l00691"></a><span class="lineno">  691</span>&#160;                                  pqCentroidsInnermostCode_,</div>
<div class="line"><a name="l00692"></a><span class="lineno">  692</span>&#160;                                  coarseIndices,</div>
<div class="line"><a name="l00693"></a><span class="lineno">  693</span>&#160;                                  useFloat16LookupTables_,</div>
<div class="line"><a name="l00694"></a><span class="lineno">  694</span>&#160;                                  <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a319568b832518392fed33ea4f8bfc613">bytesPerVector_</a>,</div>
<div class="line"><a name="l00695"></a><span class="lineno">  695</span>&#160;                                  numSubQuantizers_,</div>
<div class="line"><a name="l00696"></a><span class="lineno">  696</span>&#160;                                  numSubQuantizerCodes_,</div>
<div class="line"><a name="l00697"></a><span class="lineno">  697</span>&#160;                                  <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a96240a08b42bd1913e2286d7d514fc56">deviceListDataPointers_</a>,</div>
<div class="line"><a name="l00698"></a><span class="lineno">  698</span>&#160;                                  <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a9aedcf0e6a20b908980ae96d73461f4c">deviceListIndexPointers_</a>,</div>
<div class="line"><a name="l00699"></a><span class="lineno">  699</span>&#160;                                  <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#afb6d10e23d6448c10f472b9234e0bcab">indicesOptions_</a>,</div>
<div class="line"><a name="l00700"></a><span class="lineno">  700</span>&#160;                                  <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a3a1c2031a4763f7d55bc8a400c63af66">deviceListLengths_</a>,</div>
<div class="line"><a name="l00701"></a><span class="lineno">  701</span>&#160;                                  <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#ae25ea0901fb628844868413f51c85bda">maxListLength_</a>,</div>
<div class="line"><a name="l00702"></a><span class="lineno">  702</span>&#160;                                  k,</div>
<div class="line"><a name="l00703"></a><span class="lineno">  703</span>&#160;                                  outDistances,</div>
<div class="line"><a name="l00704"></a><span class="lineno">  704</span>&#160;                                  outIndices,</div>
<div class="line"><a name="l00705"></a><span class="lineno">  705</span>&#160;                                  <a class="code" href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">resources_</a>);</div>
<div class="line"><a name="l00706"></a><span class="lineno">  706</span>&#160;}</div>
<div class="line"><a name="l00707"></a><span class="lineno">  707</span>&#160;</div>
<div class="line"><a name="l00708"></a><span class="lineno">  708</span>&#160;} } <span class="comment">// namespace</span></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1IVFBase_html_accc4d96c14643e5f471220cb1e92ac70"><div class="ttname"><a href="classfaiss_1_1gpu_1_1IVFBase.html#accc4d96c14643e5f471220cb1e92ac70">faiss::gpu::IVFBase::numLists_</a></div><div class="ttdeci">const int numLists_</div><div class="ttdoc">Number of inverted lists we maintain. </div><div class="ttdef"><b>Definition:</b> <a href="IVFBase_8cuh_source.html#l00089">IVFBase.cuh:89</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1IVFBase_html_ae25ea0901fb628844868413f51c85bda"><div class="ttname"><a href="classfaiss_1_1gpu_1_1IVFBase.html#ae25ea0901fb628844868413f51c85bda">faiss::gpu::IVFBase::maxListLength_</a></div><div class="ttdeci">int maxListLength_</div><div class="ttdoc">Maximum list length seen. </div><div class="ttdef"><b>Definition:</b> <a href="IVFBase_8cuh_source.html#l00113">IVFBase.cuh:113</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1GpuResources_html_aa0354aa570c24e17a9f8a6a45b153ed2"><div class="ttname"><a href="classfaiss_1_1gpu_1_1GpuResources.html#aa0354aa570c24e17a9f8a6a45b153ed2">faiss::gpu::GpuResources::getDefaultStreamCurrentDevice</a></div><div class="ttdeci">cudaStream_t getDefaultStreamCurrentDevice()</div><div class="ttdoc">Calls getDefaultStream with the current device. </div><div class="ttdef"><b>Definition:</b> <a href="GpuResources_8cpp_source.html#l00023">GpuResources.cpp:23</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1IVFPQ_html_a9992b38226dc8f92ca2691582fabb675"><div class="ttname"><a href="classfaiss_1_1gpu_1_1IVFPQ.html#a9992b38226dc8f92ca2691582fabb675">faiss::gpu::IVFPQ::addCodeVectorsFromCpu</a></div><div class="ttdeci">void addCodeVectorsFromCpu(int listId, const void *codes, const long *indices, size_t numVecs)</div><div class="ttdef"><b>Definition:</b> <a href="IVFPQ_8cu_source.html#l00345">IVFPQ.cu:345</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1FlatIndex_html_a6988df17792dae30f24cc859728777e6"><div class="ttname"><a href="classfaiss_1_1gpu_1_1FlatIndex.html#a6988df17792dae30f24cc859728777e6">faiss::gpu::FlatIndex::getSize</a></div><div class="ttdeci">int getSize() const </div><div class="ttdoc">Returns the number of vectors we contain. </div><div class="ttdef"><b>Definition:</b> <a href="FlatIndex_8cu_source.html#l00045">FlatIndex.cu:45</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1IVFBase_html_a53f3c382a79b7f89630a85dfbc3a1fed"><div class="ttname"><a href="classfaiss_1_1gpu_1_1IVFBase.html#a53f3c382a79b7f89630a85dfbc3a1fed">faiss::gpu::IVFBase::listOffsetToUserIndex_</a></div><div class="ttdeci">std::vector&lt; std::vector&lt; long &gt; &gt; listOffsetToUserIndex_</div><div class="ttdef"><b>Definition:</b> <a href="IVFBase_8cuh_source.html#l00125">IVFBase.cuh:125</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1FlatIndex_html"><div class="ttname"><a href="classfaiss_1_1gpu_1_1FlatIndex.html">faiss::gpu::FlatIndex</a></div><div class="ttdoc">Holder of GPU resources for a particular flat index. </div><div class="ttdef"><b>Definition:</b> <a href="FlatIndex_8cuh_source.html#l00021">FlatIndex.cuh:21</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a74dbc09519c9c14479b2d18f2e5042e8"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a74dbc09519c9c14479b2d18f2e5042e8">faiss::gpu::Tensor::view</a></div><div class="ttdeci">__host__ __device__ Tensor&lt; T, SubDim, InnerContig, IndexT, PtrTraits &gt; view(DataPtrType at)</div><div class="ttdef"><b>Definition:</b> <a href="Tensor-inl_8cuh_source.html#l00632">Tensor-inl.cuh:632</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1IVFBase_html"><div class="ttname"><a href="classfaiss_1_1gpu_1_1IVFBase.html">faiss::gpu::IVFBase</a></div><div class="ttdoc">Base inverted list functionality for IVFFlat and IVFPQ. </div><div class="ttdef"><b>Definition:</b> <a href="IVFBase_8cuh_source.html#l00025">IVFBase.cuh:25</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1GpuResources_html"><div class="ttname"><a href="classfaiss_1_1gpu_1_1GpuResources.html">faiss::gpu::GpuResources</a></div><div class="ttdef"><b>Definition:</b> <a href="GpuResources_8h_source.html#l00021">GpuResources.h:21</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1IVFBase_html_a3a1c2031a4763f7d55bc8a400c63af66"><div class="ttname"><a href="classfaiss_1_1gpu_1_1IVFBase.html#a3a1c2031a4763f7d55bc8a400c63af66">faiss::gpu::IVFBase::deviceListLengths_</a></div><div class="ttdeci">thrust::device_vector&lt; int &gt; deviceListLengths_</div><div class="ttdef"><b>Definition:</b> <a href="IVFBase_8cuh_source.html#l00110">IVFBase.cuh:110</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1IVFPQ_html_adb58eeacdceb0e0fde1820ca7f116e05"><div class="ttname"><a href="classfaiss_1_1gpu_1_1IVFPQ.html#adb58eeacdceb0e0fde1820ca7f116e05">faiss::gpu::IVFPQ::isSupportedPQCodeLength</a></div><div class="ttdeci">static bool isSupportedPQCodeLength(int size)</div><div class="ttdoc">Returns true if we support PQ in this size. </div><div class="ttdef"><b>Definition:</b> <a href="IVFPQ_8cu_source.html#l00070">IVFPQ.cu:70</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1IVFBase_html_a9aedcf0e6a20b908980ae96d73461f4c"><div class="ttname"><a href="classfaiss_1_1gpu_1_1IVFBase.html#a9aedcf0e6a20b908980ae96d73461f4c">faiss::gpu::IVFBase::deviceListIndexPointers_</a></div><div class="ttdeci">thrust::device_vector&lt; void * &gt; deviceListIndexPointers_</div><div class="ttdef"><b>Definition:</b> <a href="IVFBase_8cuh_source.html#l00106">IVFBase.cuh:106</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1GpuResources_html_a00cb7bcbc5f1a00da673f30749149b12"><div class="ttname"><a href="classfaiss_1_1gpu_1_1GpuResources.html#a00cb7bcbc5f1a00da673f30749149b12">faiss::gpu::GpuResources::getBlasHandleCurrentDevice</a></div><div class="ttdeci">cublasHandle_t getBlasHandleCurrentDevice()</div><div class="ttdoc">Calls getBlasHandle with the current device. </div><div class="ttdef"><b>Definition:</b> <a href="GpuResources_8cpp_source.html#l00018">GpuResources.cpp:18</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1GpuResources_html_a1dda2dc3db1bd62cde6657c5cdbfb6e1"><div class="ttname"><a href="classfaiss_1_1gpu_1_1GpuResources.html#a1dda2dc3db1bd62cde6657c5cdbfb6e1">faiss::gpu::GpuResources::getMemoryManagerCurrentDevice</a></div><div class="ttdeci">DeviceMemory &amp; getMemoryManagerCurrentDevice()</div><div class="ttdoc">Calls getMemoryManager for the current device. </div><div class="ttdef"><b>Definition:</b> <a href="GpuResources_8cpp_source.html#l00033">GpuResources.cpp:33</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1IVFPQ_html_ab1e07b04b25569cc58c5f3f033f4dab3"><div class="ttname"><a href="classfaiss_1_1gpu_1_1IVFPQ.html#ab1e07b04b25569cc58c5f3f033f4dab3">faiss::gpu::IVFPQ::classifyAndAddVectors</a></div><div class="ttdeci">int classifyAndAddVectors(Tensor&lt; float, 2, true &gt; &amp;vecs, Tensor&lt; long, 1, true &gt; &amp;indices)</div><div class="ttdef"><b>Definition:</b> <a href="IVFPQ_8cu_source.html#l00118">IVFPQ.cu:118</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a6dc00c182a92389b74c89ba7fcab40d3"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a6dc00c182a92389b74c89ba7fcab40d3">faiss::gpu::Tensor::copyFrom</a></div><div class="ttdeci">__host__ void copyFrom(Tensor&lt; T, Dim, InnerContig, IndexT, PtrTraits &gt; &amp;t, cudaStream_t stream)</div><div class="ttdoc">Copies a tensor into ourselves; sizes must match. </div><div class="ttdef"><b>Definition:</b> <a href="Tensor-inl_8cuh_source.html#l00130">Tensor-inl.cuh:130</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1IVFPQ_html_ab0c458aab9a3d903f31b0e63ce16e623"><div class="ttname"><a href="classfaiss_1_1gpu_1_1IVFPQ.html#ab0c458aab9a3d903f31b0e63ce16e623">faiss::gpu::IVFPQ::query</a></div><div class="ttdeci">void query(Tensor&lt; float, 2, true &gt; &amp;queries, int nprobe, int k, Tensor&lt; float, 2, true &gt; &amp;outDistances, Tensor&lt; long, 2, true &gt; &amp;outIndices)</div><div class="ttdef"><b>Definition:</b> <a href="IVFPQ_8cu_source.html#l00516">IVFPQ.cu:516</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1IVFPQ_html_a3e8bff50f894c243c62e832f923e88e7"><div class="ttname"><a href="classfaiss_1_1gpu_1_1IVFPQ.html#a3e8bff50f894c243c62e832f923e88e7">faiss::gpu::IVFPQ::getPQCentroids</a></div><div class="ttdeci">Tensor&lt; float, 3, true &gt; getPQCentroids()</div><div class="ttdef"><b>Definition:</b> <a href="IVFPQ_8cu_source.html#l00591">IVFPQ.cu:591</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1IVFBase_html_a878114abdba07c9cf7735f9c0ed594c3"><div class="ttname"><a href="classfaiss_1_1gpu_1_1IVFBase.html#a878114abdba07c9cf7735f9c0ed594c3">faiss::gpu::IVFBase::quantizer_</a></div><div class="ttdeci">FlatIndex * quantizer_</div><div class="ttdoc">Quantizer object. </div><div class="ttdef"><b>Definition:</b> <a href="IVFBase_8cuh_source.html#l00083">IVFBase.cuh:83</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1IVFPQ_html_adcee5dbf48c3cb6b8a67f5f392e155fd"><div class="ttname"><a href="classfaiss_1_1gpu_1_1IVFPQ.html#adcee5dbf48c3cb6b8a67f5f392e155fd">faiss::gpu::IVFPQ::setPrecomputedCodes</a></div><div class="ttdeci">void setPrecomputedCodes(bool enable)</div><div class="ttdoc">Enable or disable pre-computed codes. </div><div class="ttdef"><b>Definition:</b> <a href="IVFPQ_8cu_source.html#l00100">IVFPQ.cu:100</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1IVFPQ_html_a5b349dd021b11b5f48531825359b0657"><div class="ttname"><a href="classfaiss_1_1gpu_1_1IVFPQ.html#a5b349dd021b11b5f48531825359b0657">faiss::gpu::IVFPQ::getListCodes</a></div><div class="ttdeci">std::vector&lt; unsigned char &gt; getListCodes(int listId) const </div><div class="ttdoc">Return the list codes of a particular list back to the CPU. </div><div class="ttdef"><b>Definition:</b> <a href="IVFPQ_8cu_source.html#l00583">IVFPQ.cu:583</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a6699c311648457f257afa340c61f417c"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">faiss::gpu::Tensor::getSize</a></div><div class="ttdeci">__host__ __device__ IndexT getSize(int i) const </div><div class="ttdef"><b>Definition:</b> <a href="Tensor_8cuh_source.html#l00222">Tensor.cuh:222</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1IVFBase_html_a96240a08b42bd1913e2286d7d514fc56"><div class="ttname"><a href="classfaiss_1_1gpu_1_1IVFBase.html#a96240a08b42bd1913e2286d7d514fc56">faiss::gpu::IVFBase::deviceListDataPointers_</a></div><div class="ttdeci">thrust::device_vector&lt; void * &gt; deviceListDataPointers_</div><div class="ttdef"><b>Definition:</b> <a href="IVFBase_8cuh_source.html#l00102">IVFBase.cuh:102</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a50411ce4d0fa32ef715e3321b6e33212"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a50411ce4d0fa32ef715e3321b6e33212">faiss::gpu::Tensor::data</a></div><div class="ttdeci">__host__ __device__ DataPtrType data()</div><div class="ttdoc">Returns a raw pointer to the start of our data. </div><div class="ttdef"><b>Definition:</b> <a href="Tensor_8cuh_source.html#l00174">Tensor.cuh:174</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1IVFBase_html_a05e6400358ec1f529a67209d3f24cc63"><div class="ttname"><a href="classfaiss_1_1gpu_1_1IVFBase.html#a05e6400358ec1f529a67209d3f24cc63">faiss::gpu::IVFBase::resources_</a></div><div class="ttdeci">GpuResources * resources_</div><div class="ttdoc">Collection of GPU resources that we use. </div><div class="ttdef"><b>Definition:</b> <a href="IVFBase_8cuh_source.html#l00080">IVFBase.cuh:80</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html">faiss::gpu::Tensor</a></div><div class="ttdoc">Our tensor type. </div><div class="ttdef"><b>Definition:</b> <a href="Tensor_8cuh_source.html#l00028">Tensor.cuh:28</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1IVFBase_html_a319568b832518392fed33ea4f8bfc613"><div class="ttname"><a href="classfaiss_1_1gpu_1_1IVFBase.html#a319568b832518392fed33ea4f8bfc613">faiss::gpu::IVFBase::bytesPerVector_</a></div><div class="ttdeci">const int bytesPerVector_</div><div class="ttdoc">Number of bytes per vector in the list. </div><div class="ttdef"><b>Definition:</b> <a href="IVFBase_8cuh_source.html#l00092">IVFBase.cuh:92</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1IVFBase_html_acc695610c9513952b8d234dc0db78e5c"><div class="ttname"><a href="classfaiss_1_1gpu_1_1IVFBase.html#acc695610c9513952b8d234dc0db78e5c">faiss::gpu::IVFBase::updateDeviceListInfo_</a></div><div class="ttdeci">void updateDeviceListInfo_(cudaStream_t stream)</div><div class="ttdoc">Update all device-side list pointer and size information. </div><div class="ttdef"><b>Definition:</b> <a href="IVFBase_8cu_source.html#l00136">IVFBase.cu:136</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1FlatIndex_html_a12058744ffb3fbcbb047872449269c06"><div class="ttname"><a href="classfaiss_1_1gpu_1_1FlatIndex.html#a12058744ffb3fbcbb047872449269c06">faiss::gpu::FlatIndex::getVectorsFloat32Ref</a></div><div class="ttdeci">Tensor&lt; float, 2, true &gt; &amp; getVectorsFloat32Ref()</div><div class="ttdoc">Returns a reference to our vectors currently in use. </div><div class="ttdef"><b>Definition:</b> <a href="FlatIndex_8cu_source.html#l00077">FlatIndex.cu:77</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1IVFBase_html_afb6d10e23d6448c10f472b9234e0bcab"><div class="ttname"><a href="classfaiss_1_1gpu_1_1IVFBase.html#afb6d10e23d6448c10f472b9234e0bcab">faiss::gpu::IVFBase::indicesOptions_</a></div><div class="ttdeci">const IndicesOptions indicesOptions_</div><div class="ttdoc">How are user indices stored on the GPU? </div><div class="ttdef"><b>Definition:</b> <a href="IVFBase_8cuh_source.html#l00095">IVFBase.cuh:95</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1HostTensor_html"><div class="ttname"><a href="classfaiss_1_1gpu_1_1HostTensor.html">faiss::gpu::HostTensor</a></div><div class="ttdef"><b>Definition:</b> <a href="HostTensor_8cuh_source.html#l00020">HostTensor.cuh:20</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1IVFBase_html_a2facc7285107de1f24d3471cbcf15f26"><div class="ttname"><a href="classfaiss_1_1gpu_1_1IVFBase.html#a2facc7285107de1f24d3471cbcf15f26">faiss::gpu::IVFBase::deviceListData_</a></div><div class="ttdeci">std::vector&lt; std::unique_ptr&lt; DeviceVector&lt; unsigned char &gt; &gt; &gt; deviceListData_</div><div class="ttdef"><b>Definition:</b> <a href="IVFBase_8cuh_source.html#l00119">IVFBase.cuh:119</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1DeviceTensor_html"><div class="ttname"><a href="classfaiss_1_1gpu_1_1DeviceTensor.html">faiss::gpu::DeviceTensor&lt; float, 3, true &gt;</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1IVFPQ_html_a18cfe8bf2178468f3372727d0b0bbc33"><div class="ttname"><a href="classfaiss_1_1gpu_1_1IVFPQ.html#a18cfe8bf2178468f3372727d0b0bbc33">faiss::gpu::IVFPQ::IVFPQ</a></div><div class="ttdeci">IVFPQ(GpuResources *resources, FlatIndex *quantizer, int numSubQuantizers, int bitsPerSubQuantizer, float *pqCentroidData, IndicesOptions indicesOptions, bool useFloat16LookupTables, MemorySpace space)</div><div class="ttdef"><b>Definition:</b> <a href="IVFPQ_8cu_source.html#l00033">IVFPQ.cu:33</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1IVFBase_html_aba3e3cfa469e5187f2d553fff10e0250"><div class="ttname"><a href="classfaiss_1_1gpu_1_1IVFBase.html#aba3e3cfa469e5187f2d553fff10e0250">faiss::gpu::IVFBase::dim_</a></div><div class="ttdeci">const int dim_</div><div class="ttdoc">Expected dimensionality of the vectors. </div><div class="ttdef"><b>Definition:</b> <a href="IVFBase_8cuh_source.html#l00086">IVFBase.cuh:86</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1IVFBase_html_a5027720549de98f4e609d6339099df35"><div class="ttname"><a href="classfaiss_1_1gpu_1_1IVFBase.html#a5027720549de98f4e609d6339099df35">faiss::gpu::IVFBase::addIndicesFromCpu_</a></div><div class="ttdeci">void addIndicesFromCpu_(int listId, const long *indices, size_t numVecs)</div><div class="ttdoc">Shared function to copy indices from CPU to GPU. </div><div class="ttdef"><b>Definition:</b> <a href="IVFBase_8cu_source.html#l00243">IVFBase.cu:243</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1IVFPQ_html_a0eedf0295ad73125ee1254173a176674"><div class="ttname"><a href="classfaiss_1_1gpu_1_1IVFPQ.html#a0eedf0295ad73125ee1254173a176674">faiss::gpu::IVFPQ::isSupportedNoPrecomputedSubDimSize</a></div><div class="ttdeci">static bool isSupportedNoPrecomputedSubDimSize(int dims)</div><div class="ttdef"><b>Definition:</b> <a href="IVFPQ_8cu_source.html#l00095">IVFPQ.cu:95</a></div></div>
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